Estimation of maximal lactate steady state using the sweat lactate sensor

A simple, non-invasive algorithm for maximal lactate steady state (MLSS) assessment has not been developed. We examined whether MLSS can be estimated from the sweat lactate threshold (sLT) using a novel sweat lactate sensor for healthy adults, with consideration of their exercise habits. Fifteen adults representing diverse fitness levels were recruited. Participants with/without exercise habits were defined as trained/untrained, respectively. Constant-load testing for 30 min at 110%, 115%, 120%, and 125% of sLT intensity was performed to determine MLSS. The tissue oxygenation index (TOI) of the thigh was also monitored. MLSS was not fully estimated from sLT, with 110%, 115%, 120%, and 125% of sLT in one, four, three, and seven participants, respectively. The MLSS based on sLT was higher in the trained group as compared to the untrained group. A total of 80% of trained participants had an MLSS of 120% or higher, while 75% of untrained participants had an MLSS of 115% or lower based on sLT. Furthermore, compared to untrained participants, trained participants continued constant-load exercise even if their TOI decreased below the resting baseline (P < 0.01). MLSS was successfully estimated using sLT, with 120% or more in trained participants and 115% or less in untrained participants. This suggests that trained individuals can continue exercising despite decreases in oxygen saturation in lower extremity skeletal muscles.


Measures
All (n = 15) Trained (n = 11) Untrained (n = 4) Mean difference p-value 95% CI Cohen'd  Table 2. Exercise data of participants at each load (mean ± standard deviation). Exercise completion: number of participants who were able to achieve 30 min of constant-load exercise. MLSS: number of participants who were greatest load among the loads in which blood lactate values at the end of exercise (30 min) increased within 1 mM, compared to those at 10 min after exercise initiation. The value of "measures at the end of the exercise" is exercise completion. % HRmax heart rate/maximal heart rate, % peak VO 2 oxygen uptake/ peak oxygen uptake, bpm beats per min, HR heart rate, MLSS maximal lactate steady state, sLT sweat lactate threshold, VO 2 /kg oxygen uptake/weight. MLSS based on sLT in participants with daily exercise. Next, the effect of daily exercise on MLSS based on sLT was investigated. The MLSS based on sLT in trained participants was higher than in untrained participants ( Table 1). The MLSS for the trained group accounted for more than 120% of the sLT, while the untrained group accounted for less than 115% of the sLT (P = 0.03, φ = 0.55). These findings suggested that MLSS was 120% or more of sLT in regularly trained participants and 115% or less of sLT in untrained participants. To determine a physiological contributor to the difference in MLSS based on sLT between regularly trained and untrained participants, we investigated the relationship between blood lactate accumulation during constant loading tests and a decrease in oxygen saturation in intra-skeletal muscles.
ΔTOI and blood lactate levels in participants with daily exercise. ΔTOI strongly correlated with blood lactate level at the end of exercise (trained: r = −0.7, untrained: r = −0.8, Fig. 4 and Online Fig. 8). The plot revealed that the constant-load exercise was discontinued in the untrained group only when ΔTOI was lower than the resting value ( Fig. 4, red triangle). In contrast, in regularly trained participants, the exercise continued until up to a 15% decrease in ΔTOI, as compared to the resting value ( Fig. 4, black circle). Moreover, a steeper increase in blood lactate was associated with a decrease in ΔTOI in untrained participants as compared to the trained group, suggesting that a slight decrease in ΔTOI immediately contributed to the increase in blood lactate The optimal cut-off value for completion of the constant-load exercise was estimated to occur at ΔTOI of −17% (sensitivity: 0.97, specificity: 1.00) and −2.6% (sensitivity: 0.88, specificity: 1.00) in trained and untrained participants, respectively, by the ROC curve analysis (Online Fig. 9). Table 3. Exercise data of participants at MLSS (mean ± standard deviation). % HRmax heart rate/maximal heart rate, % peak VO 2 oxygen uptake/peak oxygen uptake, bpm beats per min, HR heart rate, MLSS maximal lactate steady state, VO 2 /kg oxygen uptake/weight. www.nature.com/scientificreports/ Figure 4. Correlation between change in tissue oxygenation index (TOI) and blood lactate. The black circle represents trained participants, who showed a good correlation between ΔTOI and blood lactate level (y = −0.2859x + 3.035, r = −0.7, P < 0.01). The red triangle represents untrained participants, who showed a good correlation between ΔTOI and blood lactate (y = −0.479x + 5.2349, r = −0.8, P < 0.01). A steeper increase in blood lactate level was associated with a decrease in ΔTOI in untrained participants as compared to trained participants. ΔTOI TOI (pre-post).

Discussion
This prospective study provided novel evidence of successful MLSS estimation via sLT calculation by a wearable and non-invasive sweat lactate sensor, with consideration of daily exercise. sLT can be determined independent of the amount of sweating using a sweat lactate sensor on the upper arm 13 . The device also determines the inflection point but not the absolute sweat lactate value [13][14][15] . The most significant result was that MLSS approximated 120-125% of sLT in regularly trained participants and 115% or less of sLT in untrained participants. The difference in the physiological response to the decrease in oxygen saturation in lower limb skeletal muscle may contribute to this relationship between MLSS and sLT. Determining MLSS requires multiple constant-load exercise tests. MLSS was defined as the greatest load among the loads in which blood lactate values at the end of exercise (30 min) increased within 1 mM, compared to those at 10 min after exercise initiation 4,11,12 Therefore, methods have been developed to estimate MLSS using LT and OBLA, with MLSS of 124-127% of LT load 4,11 and 90% of OBLA load 12 , mainly for athletes 4,11,12 . Additionally, the Functional Threshold Power (FTP) used by cyclists has been determined through several constant submaximal load tests performed on separate days as well as MLSS 16 . FTP is a non-invasive method for measuring training intensity, which correlates well with MLSS 17 . However, LT and OBLA require frequent blood lactate measurements and exercise cessation to collect blood samples. FTP is an index specific to cyclists that requires several constant submaximal load tests performed on separate days and frequent blood lactate measurements during exercise. Therefore, although exercise with appropriate dosage and intensity is essential for maintaining good health in all generations, MLSS measurements are impractical, particularly in non-athletes or those without exercise habits.
A sweat sensor was developed to monitor sweat lactate values in real-time during progressive exercise in a clinical setting and for sports use. Our sensor is highly flexible and can be smoothly adjusted to curved surfaces using PET substrates. The upper arm and forehead are appropriate sites to monitor the lactate levels in sweat due to a high-sweat rate during exercise, smooth skin surfaces for sensor placement, and noninterference during pedaling tasks [13][14][15] . Especially in healthy subjects, the upper arm has been used because of its simplicity of attachment and minimal interference. sLT defined as the first significant increase in sweat lactate concentration above baseline based on graphical plots, is consistent with LT calculated from blood samples and ventilatory threshold assessed with exhaled gas analysis 13 . In this study, MLSS was successfully estimated via sLT, with 120-125% of sLT in regularly trained participants and 115% or less in untrained participants. Blood lactate, % peak VO 2 , and % HRmax at the end of exercise at MLSS load were consistent with data from previous reports 1,2,8,17,18 . Reportedly, 124-127% of blood LT intensity at the running speed was the MLSS intensity in track and field athletes or cyclists 4,11 . These previous findings were consistent with MLSS load based on sLT in participants who regularly exercised. In untrained participants, MLSS approximated 115% or less of sLT. Assessment of appropriate exercise dosage and intensity should be further targeted for the well-being of non-athletes. MLSS, estimated in a simple and non-invasive manner using a sweat lactate sensor, could be used for health maintenance in non-athletes.
To determine a physiological contributor to the difference in MLSS based on sLT between regularly trained and untrained participants, we investigated the relationship between blood lactate accumulation during constant loading tests and a decrease in oxygen saturation in intra-skeletal muscles. The constant-load exercise was completed for 30 min in trained participants without blood lactate accumulation, even with substantial decreases in oxygen saturation in lower limb skeletal muscles. This finding suggests that training enables constant-load exercise for long periods, even at loads relatively greater than an anaerobic threshold, at which oxygen saturation in intra-skeletal muscles can be preserved. In contrast, a steeper increase in blood lactate was associated with a decrease in ΔTOI in the untrained group as compared to the trained group, suggesting that a slight decrease in ΔTOI immediately contributes to blood lactate accumulation. Exercise tolerance improves through biological responses, such as increased blood flow in skeletal muscles 19 , improved mitochondrial function 20 , and a shift from IIb to IIa in skeletal muscle subsets 21 . These biological responses are induced by the activation of hypoxic response signals following oxygen saturation reduction in skeletal muscles during exercise [22][23][24][25][26][27] . Therefore, the extent and variability of oxygen saturation reduction during exercise may be related to training effectiveness. Training results in the acquisition of hypoxic tolerance in skeletal muscles, causing increases in exercise endurance and enabling exercise with stronger intensity. Positive feedback between the decrease in oxygen saturation in skeletal muscles and improvement in exercise tolerance could maximize training benefits.
Limitations. Our findings should be interpreted with consideration of the following limitations. First, because of the observational study design, we cannot exclude the influence of selection bias. Second, our study included a relatively small number of cases, particularly for the untrained group, and primarily healthy collegeage male individuals. Further research should include untrained participants and women. Third, constant-load exercises at 130% of sLT load were not performed in this study. Finally, there was a possibility of non-response in the sweat lactate sensor owing to a lack of sweat during exercise. Particularly, older adults and women sweat less 28 . Therefore, in such cases, adjusting exercise parameters to promote sweating is necessary. However, sLT could be clearly determined in all participants in this study.

Conclusions
By dividing the participants into trained and untrained groups, MLSS was successfully estimated using sLT, with 120% or more of the sLT load in trained participants and 115% or less in untrained participants. This finding may involve the ability of an individual to continue exercising despite a decrease in oxygen saturation in the lower extremity skeletal muscles. This novel actualized measurement of sLT is expected to enable non-invasive MLSS estimation. This simple and non-invasive algorithm can be used as a convenient indicator of good health maintenance for non-athletes and a potential guide for training athletes.

Methods
Participants. Fifteen healthy adults representing a broad spectrum of fitness levels, regardless of exercise habits, were recruited between May and September 2022. Participants with/without exercise habits were defined as "trained", and "untrained, " respectively. Exercise habit was defined as > 75 min per week of exercise at vigorous intensity 29 . The inclusion criteria were as follows: no underlying or pre-existing cardiovascular, respiratory, or metabolic diseases; no athletic injuries; non-smokers; and no dietary supplements or medication habits of any type. The study protocol was approved by the Institutional Review Board of the Keio University School of Medicine (approval number: 20190229) and conducted in accordance with the principles of the Declaration of Helsinki. All participants provided informed consent because the Institutional Review Board approved the use of oral consent, in accordance with the Japanese guidelines for clinical research.

Experimental procedure
A flowchart of the study protocol is shown in Fig. 6. First, the Ramp stress test was performed using an electromagnetically braked ergometer (StrengthErgo8 V2; Fukuda Denshi Co., Ltd., Tokyo, Japan) with a sweat lactate sensor (Grace Imaging Inc., Tokyo, Japan), an exhaled gas analyzer (Aeromonitor AE-301S; Minato Medical Science Co., Ltd., Osaka, Japan), and a heart rate (HR) monitor (POLAR H10 N; Polar Electro Japan, Tokyo, Japan). Subsequently, constant-load exercise was performed for 30 min at 125%, 120%, 115%, and 110% of sLT intensity in this order. An electromagnetically-braked ergometer was used during the exercise to determine MLSS 12 . At least 24 h were allowed between each test (mean: 7.0 ± 2.9 days) 5 . During constant-load exercise, an exhaled gas analyzer, HR monitor, and near-infrared spectroscopy (NIRS) monitor (NIRO-200NX; Hamamatsu Photonics K.K., Hamamatsu, Japan) were attached. Blood lactate values were obtained via auricular pricking and gentle squeezing of the ear lobe using a blood lactate analyzer (Lactate Pro 2, ARKRAY Inc., Kyoto, Japan). Blood lactate levels were measured before exercise and every 5 min during exercise.
Exercise test protocol. Participants avoided caffeine and alcohol consumption, which would cause fatigue, the day before testing. After measuring resting data for 2 min, participants performed a warm-up exercise for 2 min at a 50-W load and then exercised at increasing intensities until they could no longer maintain the pedaling rate (volitional exhaustion). The resistance was increased in 25-W increments from 50-W at 1-min intervals. Rotational speed was maintained at 70 rotations per min (rpm).
sLT determination. A sweat lactate sensor quantifies sweat lactate concentration as a value of current because it reacts with sweat lactate and generates an electric current. The value of current can be obtained as continuous data within 0.1-80 μA in 0.1-μA increments 13 . Further, we investigated whether the lactate values (current values) of sweat obtained from this sensor could show a relative difference significant enough to determine this inflection point under various sweat environments (pH, temperature, and ionic conductivity) with several solutions that were close in composition to actual sweat. Regarding the pH and temperature of human sweat, it has been reported that sweat has a pH of 5-7 and a skin temperature of 25-37 °C [30][31][32] . Therefore, the electrochemical characterization of the lactate sensor chip was performed using L-lactic acid solutions in 0, 2.5, 5, 10, and 20 mM prepared in 0.1 mol/L phosphate buffer solution (PBS) under different temperatures (25, 31, 36 °C) and pH (5, 6, 7, and 8). Then, the three lactate sensor tips were evaluated in each condition using chronoamperometry at an applied voltage of 0.16 V (versus Ag/AgCl). Next, the major electrolytes in sweat are Na, K, and Cl. Generally, NaCl varies from 10 to 90 mM and KCl from 2 to 8 mM during exercise 30 . Therefore, the sensor evaluated a significant response to l-lactic acid solution in 10 mM even in the presence of NaCl (10, 25, 50, 100 mM) and KCl (2.5, 5, 10 mM). After calibration using saline for 2 or 3 min, the sensor chip connected to the sensor device was attached to the superior right upper limb of the participant 13,14 , which was cleaned with an alcohol-free cloth. The upper arm has a high-sweat rate during physical excursions 33 . In addition, it is a site that does not interfere with exercise during pedaling tasks. Additionally, data were recorded at a 1-Hz sampling frequency for mobile applications with a Bluetooth connection. Recorded data were converted to moving average values over 13-s intervals and individually underwent zero correction using the baseline value. sLT was defined as the first significant increase in sweat lactate concentration above baseline based on a graphical plot (Fig. 7) [13][14][15]34 .

MLSS determination.
Blood lactate was measured before exercise and every 5 min during constant-load exercise for 30 min at 110%, 115%, 120%, and 125% of sLT intensity. The rotational speed was set at 70 rpm. The criteria that did not achieve the exercise and exceeded the MLSS included participants who could not finish the trial due to fatigue, but could not maintain bicycle pedaling at 70 rpm, as well as participants who could finish 30 min of exercise but had an increase in blood lactate of more than 1 mM from 10 min after exercise initiation to the end of the exercise. MLSS was defined as the greatest load among the loads in which blood lactate values at the end of exercise (30 min) increased within 1 mM, compared to those at 10 min after exercise initiation 12 (Fig. 8).

Measurement data.
On the first day of measurement, body weight, body fat, and skeletal muscle mass were measured using In-Body (InBody470; InBody Japan Inc., Tokyo, Japan). Expired gas flow was measured using a breath-by-breath automated system. Three calibration processes were performed on the system: flow volume sensor, gas analyzer, and delay time calibration. Parameters of respiratory gas exchange, including ventilation (VE), oxygen uptake (VO 2 ), and carbon dioxide production (VCO 2 ), were continuously monitored and measured using a 10-s average. Skeletal muscle oxygenation in the right thigh was measured using NIRS spectroscopy. www.nature.com/scientificreports/ The monitor consists of a light-sending probe and a light-receiving probe. Near-infrared light emitted from the light-sending unit is absorbed by skeletal muscle tissue, and changes in the intensity of the light returned to the light-receiving unit enable tissue oxygenation measurement 35 . A pair of probes was attached 4 cm apart on the skin over the vastus lateralis muscle in the distal third of the thigh 36,37 and then covered and secured with tape 38 . In this study, tissue hemoglobin oxygen saturation (tissue oxygenation index [TOI]), calculated using the spatially resolved spectroscopy method, was assessed 39,40 . Statistical analyses. All data are presented as means and standard deviations. The obtained HR and VO 2 were calculated as a percentage of the maximal HR (% HRmax) and peak VO 2 (% peak VO 2 ). The relationships between the sLT and ventilatory threshold (VT) were investigated using Pearson's correlations. Additionally, the Bland and Altman technique was applied to verify the similarities among the different methods. This comparison is a graphical representation of the difference between the methods and the average of these methods. As previous reports have shown that MLSS is 120% or more of LT intensity, we divided our cohort into two groups using the cut-off of 120% of sLT intensity 4,11,12 . Unpaired t-tests and Chi-squared tests were used to compare participant characteristics between the two groups. The correlation value was used to determine the relationship between the relative change in TOI from baseline (ΔTOI) and blood lactate at the end of the exercise. Unpaired t-tests were used to compare ΔTOI across trained and untrained participants.